Large scale radio frequency signal information processing and analysis system using bin-wise processing

ABSTRACT

A large-scale radio frequency signal information processing and analysis system that provides advanced signal analysis for telecommunication applications, including band capacity and geographical density determinations and detection, classification, identification, and geolocation of signals across a wide range of frequencies and across broad geographical areas. The system may utilize a range of novel algorithms for bin-wise processing, Rayleigh distribution analysis, telecommunication signal classification, receiver anomaly detection, transmitter density estimation, transmitter detection and location, geolocation analysis, telecommunication activity estimation, telecommunication utilization estimation, frequency utilization estimation, and data interpolation.

CROSS-REFERENCE TO RELATED APPLICATIONS application Ser. No. Date FiledTitle Current Herewith LARGE SCALE RADIO FREQUENCY application SIGNALINFORMATION PROCESSING AND ANALYSIS SYSTEM USING BIN-WISE PROCESSING Isa continuation of: 16/808,327 Mar. 3, 2020 LARGE SCALE RADIO FREQUENCYSIGNAL INFORMATION PROCESSING AND ANALYSIS SYSTEM USING BIN-WISEPROCESSING which is a continuation of: 16/384,621 Apr. 15, 2019 LARGESCALE RADIO FREQUENCY U.S. Pat. Issue Date: SIGNAL INFORMATIONPROCESSING No. Mar. 3, 2020 AND ANALYSIS SYSTEM 10,582,401 which is acontinuation-in-part of: 15/991,540 May 29, 2018 SYSTEM AND METHODS FORU.S. Pat. Issue Date: DETECTING AND CHARACTERIZING No. Jul. 2, 2019ELECTROMAGNETIC EMISSIONS 10,338,118 which claims benefit of, andpriority to: 62/656,781 Apr. 12, 2018 SYSTEM AND METHODS FOR DETECTINGAND CHARACTERIZING ELECTROMAGNETIC EMISSIONS Current Herewith LARGESCALE RADIO FREQUENCY application SIGNAL INFORMATION PROCESSING ANDANALYSIS SYSTEM USING BIN-WISE PROCESSING Is a continuation of:16/808,327 Mar. 3, 2020 LARGE SCALE RADIO FREQUENCY SIGNAL INFORMATIONPROCESSING AND ANALYSIS SYSTEM USING BIN-WISE PROCESSING which is acontinuation of: 16/384,621 Apr. 15, 2019 LARGE SCALE RADIO FREQUENCYU.S. Pat. Issue Date: SIGNAL INFORMATION PROCESSING No. Mar. 3, 2020 ANDANALYSIS SYSTEM 10,582,401 which is a continuation-in-part of:15/585,102 May 2, 2017 SYSTEMS AND METHODS FOR MEASURING TERRESTRIALSPECTRUM FROM SPACE which claims benefit of, and priority to: 62/305,513Mar. 8, 2016 SYSTEMS AND METHODS FOR MEASURING TERRESTRIAL SPECTRUM FROMSPACE the entire specification of each of which is incorporated hereinby reference.

BACKGROUND Field of the Art

The disclosure relates to the field of telecommunication and moreparticularly to the field of advanced radio frequency signal informationprocessing and analysis for telecommunications and other applications.

Discussion of the State of the Art

Most wireless telecommunication requires transmission and reception ofradio frequency (radio frequency signal) signals in the radio frequencysignal spectrum from frequencies of about 3 kHz to frequencies of about300 GHz. Mobile, backhaul, consumer, fixed station, and public safetycommunications, to name a few, rely on frequencies in the radiofrequency signal spectrum that are assigned, or allocated, for aparticular use. Further, certain frequency bands are licensed toparticular users in specified geographical areas.

Due to the proliferation of wireless communication applications,frequency bands for wireless communication in the radio frequency signalspectrum are becoming congested. Efforts continue to be made to increasethe efficiency of frequency usage by consolidating and assigning unusedor minimally used frequencies, and by reallocating frequencies for useas demand dictates. Additionally, transmitter output power is beingreduced to limit the effective area of a transmitted signal so thatfrequencies can be reused based on geographical diversity. Consumerdemand and trends towards high-data rate communications, which requiresthe use of increasing bandwidth and efficient transmissions methods,drive these innovations. As the complexity of wireless systems naturallyincreases to support more users in discrete bands, better systematicknowledge of the spectrum environment is needed to ensure properoperation. Indeed, proposed approaches such as dynamic spectrumallocation and cognitive radio techniques for spectrum sharing, tofurther enhance efficiency of spectrum use, require good knowledge ofthe spectral environment.

What is needed is a system for analyzing the radio frequency landscapeacross a broad range of frequencies and over a variety of geographicalareas.

SUMMARY

Accordingly, the inventor has conceived and reduced to practice, alarge-scale radio frequency signal information processing and analysissystem that provides advanced signal analysis for telecommunicationapplications, including band capacity and geographical densitydeterminations and detection, classification, identification, andgeolocation of signals across a wide range of frequencies and acrossbroad geographical areas. The system may utilize a range of novelalgorithms for bin-wise processing, Rayleigh distribution analysis,telecommunication signal classification, receiver anomaly detection,transmitter density estimation, transmitter detection and location,geolocation analysis, telecommunication activity estimation,telecommunication utilization estimation, frequency utilizationestimation, and data interpolation.

According to a preferred embodiment, a large scale radio frequencysignal information processing and analysis system is disclosed,comprising: a computing device comprising at least a memory and one ormore processors; a signal analyzer, which receives data comprising oneor more detected or confirmed signals, and uses one or more algorithmsto provide information about the radio frequency signal landscape; asignal confirmer, which receives signal information comprising at leastone suspected signal and uses one or more algorithms to confirm theexistence of suspected signals within the signal information; and asignal detector which receives signal information, and uses one or morealgorithms to detect radio frequency signals within the signalinformation.

According to an aspect of an embodiment, one of the algorithms used todetect or confirm signals is a novel implementation of a bin-wiseprocessor.

According to an aspect of an embodiment, one of the algorithms used todetect or confirm signals is a novel implementation of a Rayleighdistribution analyzer.

According to an aspect of an embodiment, one of the algorithms used todetect or confirm signals is a novel implementation of atelecommunication signal classifier.

According to an aspect of an embodiment, one of the algorithms used todetect or confirm signals is a novel implementation of a receiveranomaly analyzer.

According to an aspect of an embodiment, one of the algorithms used toanalyze signals is a novel implementation of a transmitter densityestimator.

According to an aspect of an embodiment, one of the algorithms used toanalyze signals is a novel implementation of a transmitter detector andlocator.

According to an aspect of an embodiment, one of the algorithms used toanalyze signals is a novel implementation of a geolocation analyzer.

According to an aspect of an embodiment, the geolocation analyzer usesan alternate novel method of determining location.

According to an aspect of an embodiment, one of the algorithms used toanalyze signals is a novel implementation of a telecommunicationactivity estimator.

According to an aspect of an embodiment, one of the algorithms used toanalyze signals is a novel implementation of a telecommunicationutilization estimator.

According to an aspect of an embodiment, one of the algorithms used toanalyze signals is a novel implementation of a frequency utilizationestimator.

According to an aspect of an embodiment, one of the algorithms used toanalyze signals is a novel implementation of a data interpolator.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The accompanying drawings illustrate several aspects and, together withthe description, serve to explain the principles of the inventionaccording to the aspects. It will be appreciated by one skilled in theart that the particular arrangements illustrated in the drawings aremerely exemplary, and are not to be considered as limiting of the scopeof the invention or the claims herein in any way.

FIG. 1 is a block diagram illustrating an exemplary system architectureoverview, according to an embodiment.

FIG. 2 is a block diagram illustrating an exemplary radio frequencysignal information processing and analysis system, according to apreferred embodiment.

FIG. 3 is a block diagram illustrating an exemplary signal detector,according to an aspect of a preferred embodiment.

FIG. 4 is a block diagram illustrating an exemplary signal confirmer,according to an aspect of an embodiment.

FIG. 5 is a block diagram illustrating an exemplary signal analyzer,according to an aspect of an embodiment.

FIG. 6 is a diagram illustrating a variety of sensor systems andcollection platforms for gathering radio frequency signal data.

FIG. 7 is a flow diagram illustrating an exemplary algorithm forbin-wise processing, according to an aspect of an embodiment.

FIG. 8 is a flow diagram illustrating an exemplary algorithm for aRayleigh distribution analysis, according to an aspect of an embodiment.

FIG. 9 is a flow diagram illustrating an exemplary algorithm fortelecommunication signal classification, according to an aspect of anembodiment.

FIG. 10 is a flow diagram illustrating an exemplary algorithm forreceiver anomaly detection, according to an aspect of an embodiment.

FIG. 11 is a flow diagram illustrating an exemplary algorithm fortransmitter density estimation, according to an aspect of an embodiment.

FIG. 12 is a flow diagram illustrating an exemplary algorithm fortransmitter detection and location, according to an aspect of anembodiment.

FIG. 13 is a flow diagram illustrating an exemplary algorithm forgeolocation analysis, according to an aspect of an embodiment.

FIG. 14 is a flow diagram illustrating an exemplary algorithm forgeolocation analysis using an alternate method, according to an aspectof an embodiment.

FIG. 15 is a flow diagram illustrating an exemplary algorithm fortelecommunication activity estimation, according to an aspect of anembodiment.

FIG. 16 is a flow diagram illustrating an exemplary algorithm fortelecommunication utilization estimation, according to an aspect of anembodiment.

FIG. 17 is a flow diagram illustrating an exemplary algorithm forfrequency utilization estimation, according to an aspect of anembodiment.

FIG. 18 is a flow diagram illustrating an exemplary algorithm for sampledata interpolation, according to an aspect of an embodiment.

FIG. 19 is a block diagram illustrating an exemplary hardwarearchitecture of a computing device.

FIG. 20 is a block diagram illustrating an exemplary logicalarchitecture for a client device.

FIG. 21 is a block diagram showing an exemplary architecturalarrangement of clients, servers, and external services.

FIG. 22 is another block diagram illustrating an exemplary hardwarearchitecture of a computing device.

DETAILED DESCRIPTION

The inventor has conceived, and reduced to practice, a large-scale radiofrequency signal information processing and analysis system thatprovides advanced signal analysis for telecommunication applications,including band capacity and geographical density determinations anddetection, classification, identification, and geolocation of signalsacross a wide range of frequencies and across broad geographical areas.The system may utilize a range of novel algorithms for bin-wiseprocessing, Rayleigh distribution analysis, telecommunication signalclassification, receiver anomaly detection, transmitter densityestimation, transmitter detection and location, geolocation analysis,telecommunication activity estimation, telecommunication utilizationestimation, frequency utilization estimation, and data interpolation.

One or more different aspects may be described in the presentapplication. Further, for one or more of the aspects described herein,numerous alternative arrangements may be described; it should beappreciated that these are presented for illustrative purposes only andare not limiting of the aspects contained herein or the claims presentedherein in any way. One or more of the arrangements may be widelyapplicable to numerous aspects, as may be readily apparent from thedisclosure. In general, arrangements are described in sufficient detailto enable those skilled in the art to practice one or more of theaspects, and it should be appreciated that other arrangements may beutilized and that structural, logical, software, electrical and otherchanges may be made without departing from the scope of the particularaspects. Particular features of one or more of the aspects describedherein may be described with reference to one or more particular aspectsor figures that form a part of the present disclosure, and in which areshown, by way of illustration, specific arrangements of one or more ofthe aspects. It should be appreciated, however, that such features arenot limited to usage in the one or more particular aspects or figureswith reference to which they are described. The present disclosure isneither a literal description of all arrangements of one or more of theaspects nor a listing of features of one or more of the aspects thatmust be present in all arrangements.

Headings of sections provided in this patent application and the titleof this patent application are for convenience only, and are not to betaken as limiting the disclosure in any way.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or morecommunication means or intermediaries, logical or physical.

A description of an aspect with several components in communication witheach other does not imply that all such components are required. To thecontrary, a variety of optional components may be described toillustrate a wide variety of possible aspects and in order to more fullyillustrate one or more aspects. Similarly, although process steps,method steps, algorithms or the like may be described in a sequentialorder, such processes, methods and algorithms may generally beconfigured to work in alternate orders, unless specifically stated tothe contrary. In other words, any sequence or order of steps that may bedescribed in this patent application does not, in and of itself,indicate a requirement that the steps be performed in that order. Thesteps of described processes may be performed in any order practical.Further, some steps may be performed simultaneously despite beingdescribed or implied as occurring non-simultaneously (e.g., because onestep is described after the other step). Moreover, the illustration of aprocess by its depiction in a drawing does not imply that theillustrated process is exclusive of other variations and modificationsthereto, does not imply that the illustrated process or any of its stepsare necessary to one or more of the aspects, and does not imply that theillustrated process is preferred. Also, steps are generally describedonce per aspect, but this does not mean they must occur once, or thatthey may only occur once each time a process, method, or algorithm iscarried out or executed. Some steps may be omitted in some aspects orsome occurrences, or some steps may be executed more than once in agiven aspect or occurrence.

When a single device or article is described herein, it will be readilyapparent that more than one device or article may be used in place of asingle device or article. Similarly, where more than one device orarticle is described herein, it will be readily apparent that a singledevice or article may be used in place of the more than one device orarticle.

The functionality or the features of a device may be alternativelyembodied by one or more other devices that are not explicitly describedas having such functionality or features. Thus, other aspects need notinclude the device itself.

Techniques and mechanisms described or referenced herein will sometimesbe described in singular form for clarity. However, it should beappreciated that particular aspects may include multiple iterations of atechnique or multiple instantiations of a mechanism unless notedotherwise. Process descriptions or blocks in figures should beunderstood as representing modules, segments, or portions of code whichinclude one or more executable instructions for implementing specificlogical functions or steps in the process. Alternate implementations areincluded within the scope of various aspects in which, for example,functions may be executed out of order from that shown or discussed,including substantially concurrently or in reverse order, depending onthe functionality involved, as would be understood by those havingordinary skill in the art.

Definitions

“Collection platform” or “platform” as used herein refers to a surface,structure, vehicle, portable electronic device, or other object on whicha sensor system may be mounted, in which a sensor system may be placed,or into which a sensor system may be integrated. Collection platformsmay take a variety of forms, including, but not limited to fixed objects(e.g. desks, windows, towers, buildings, billboards, etc.), portableobjects (backpacks, mobile phones, etc.), vehicles (cars, trucks, boats,etc.), small scale aircraft (e.g., drones, model airplanes, etc.),aircraft (airplanes, helicopters, etc.), balloons (hot-air balloons,weather balloons, etc.), and satellites.

“Landscape” or “radio frequency landscape” as used herein means thetotality of radio frequency signal activity in a given area, including,but not limited to, signal times, signal locations, signal directions,signal altitudes, signal sources, signal frequencies, signal powers,areas of good reception, and areas of poor reception, and may includenon-signal information such as the identification of individuals orcompanies emitting signals.

“Radio frequency” as used herein means frequencies from about 3kilohertz (3 kHz) to about 300 gigahertz (300 GHz).

“Radio frequency data” means any data associated with a radio frequencysignal.

“Radio frequency signal” as used herein means any detectable radiofrequency signals with a frequency or frequencies from about 3 kilohertz(3 kHz) to about 300 gigahertz (300 GHz).

“Sensor system” as used herein means a system capable of receiving radiofrequency signals. Although some of the embodiments herein assume acomplex sensor system such as a software-defined radio capable ofdetecting, receiving, processing, and storing radio frequency signals,the term sensor system is not so limited, and includes any systemcapable of receiving radio frequency signals from simple wire antennareceivers to sophisticated systems with directional antennas and complexcircuitry.

“Signal” as used herein means radio frequency signal unless the contextindicates otherwise.

“Signal information” or “signal data” means any information associatedwith or used to describe a signal. This may include radio frequency dataand/or metadata describing the radio frequency data (for example,information about the sensor system and collection platformconfiguration, the area over which radio frequency data was collected,the time at which data was collected, look-angles of the sensor systemor collection platform, operating characteristics of the sensor systemor collection platform, including but not limited to, location, speed,orientation, movement relative to Earth, movement relative to othersensor systems, frequencies of operation, calibration data, times ofoperation, etc.), inputs from other algorithms, and/or inputs from othersources of information (for example, a database or list of cellular basestation locations). Signal information may be from one or moregeographical locations or times.

“Telecommunication signal” or “telecom signal” means any radio frequencysignal carrying information intended to convey a message.Telecommunication signals may be analog or digital. Althoughtelecommunication signals are frequently associated with mobile phonesand mobile phone companies, the term as used herein is broader, andencompasses any form of synthetic radio frequency signal intending toconvey information, including, but not limited to, radio, television,mobile phone, WiFi, Bluetooth, or other such transmission.

Conceptual Architecture

FIG. 1 is a block diagram illustrating an exemplary system architectureoverview 100, according to an embodiment. In this overview, a radiofrequency signal information processing and analysis system 200 is shownin relation to an array of related computing devices and networkcomponents. For example, the radio frequency signal informationprocessing and analysis system 200 may be located on a computing device110 which has data storage 120 and other computer system components 130.The computing device 110 may be connected to a plurality of collectionplatforms 140 that gather radio frequency signal information forprocessing and analysis. The computing device 110 may also be connectedto a network 150 comprising other networked computers or systems 160,including but not limited to the internet, and to a variety of networkedsources of radio frequency signal information 170. This exemplary systemarchitecture by no means restricts the architecture or configuration ofthe invention, and a person of ordinary skill in the art will understandthat the invention may be implemented using a variety of computercomponents, computing devices, and network connections.

FIG. 2 is a block diagram illustrating an exemplary radio frequencysignal information processing and analysis system 200, according to apreferred embodiment. In this embodiment, the radio frequency signalinformation processing and analysis system 200 comprises a signaldetector 300, a signal confirmer, and a signal analyzer 400. The signaldetector 300 receives radio frequency signal information and analyzes itusing one or more algorithms to detect possible signals within the data.For portions of the data in which a signal is detected, that portion ofthe data is forwarded directly to the signal analyzer 500. For portionsof the data in which no signal is detected, that portion of the data issent to storage 120 or discarded. For portions of the data in which asignal is suspected, but where there may be questions about the validityof the signal, that portion of the data is forwarded to the signalconfirmer 400 for further analysis. The signal confirmer 400 receivesradio frequency signal information comprising suspected signals andanalyzes it using one or more algorithms to confirm the existence ofsuspected signals within the data. For portions of the data in which theexistence of a signal is confirmed, that portion of the data isforwarded to the signal analyzer 500. For portions of the data in whichthe existence of a signal is not confirmed, that portion of the data issent to storage 120 or discarded. The signal analyzer 500 receives radiofrequency signal information comprising at least one detected orconfirmed signal, and analyzes it using one or more algorithms designedto provide information about the radio frequency signal landscape.

FIG. 3 is a block diagram illustrating an exemplary signal detector 300,according to an aspect of an embodiment. As signal information isreceived by the signal detector 300, it is distributed to ademultiplexer 330 and one or more signal algorithms 310 a-n. Each signalalgorithm analyzes the data and outputs a result indicating whether asignal was detected (1) or not detected (0). The output of eachalgorithm 310 a-n is passed to a demultiplexer 330, which, depending onthe consensus of the algorithms 310 a-n routes the radio frequencysignal information to storage 120, to the signal confirmer 400, or tothe signal analyzer 500. In this embodiment, routing to storage 120occurs where there is a unanimous consensus of the algorithms 310 a-nthat there is no signal (00 . . . 0), routing to the signal analyzeroccurs when there is unanimous consent of the algorithms 310 a-n that asignal has been detected (11 . . . 1), and routing to the signalconfirmer 400 occurs when there is no unanimous consensus of thealgorithms as to the detection of a signal (indeterminate). However, aperson of ordinary skill in the art will recognize that unanimity ofconsensus is not required, and that the routing of the data may be basedon any combination of outputs from the one or more algorithms 310 a-n,including but not limited to a majority consensus, minority consensus,or even a single indication of the existence of a signal. Further,although this embodiment shows a particular hardware configuration usinga demultiplexer 330, a person of ordinary skill in the art willrecognize that many configurations of hardware, software, or both, mayaccomplish the same functionality.

FIG. 4 is a block diagram illustrating an exemplary signal confirmer400, according to an aspect of an embodiment. As a portion of radiofrequency signal information comprising at least one suspected signal isreceived by the signal confirmer 300, it is distributed to ademultiplexer 440 and one or more signal algorithms 410 a-n and one ormore noise algorithms 420 a-n. In this embodiment, the signal algorithms410 a-n act as positive indicators of the existence of a signal and thenoise algorithms 420 a-n act as negative indicators of the existence ofa signal. Each signal algorithm 410 a-n analyzes the data and outputs aresult indicating whether a signal was confirmed (1) or not confirmed(0). Each noise algorithm 420 a-n analyzes the data and outputs a resultindicating whether the portion of data is noise (0) or not noise (1).The output of the set of signal algorithms 410 a-n is passed to an ORlogic gate 430 a, which outputs a confirmation of signal (1) if any oneof the signal algorithms 410 a-n confirms the existence of a signal, anda non-confirmation (0) otherwise. The output of the set of noisealgorithms 420 a-n is passed to another OR logic gate 430 b, whichoutputs a confirmation of signal (1) if any one of the noise algorithms410 a-n indicates that the portion of data is not noise (1), and anon-confirmation (0) otherwise. The output of the two OR logic gates 430a-b is passed to a demultiplexer 440, which passes through the data tothe signal analyzer 500 in case of a consensus (11) of the OR logicgates that a signal is confirmed. Otherwise, the data are sent tostorage 120 or discarded. In this embodiment, sets of algorithms areused to represent positive indicators and negative indicators of signalconfirmation, and the logical conjunction (AND) of the logicaldisjunction (OR) of the results of each set of indicators is used todetermine whether a signal is confirmed. However, a person of ordinaryskill in the art will recognize that this particular logical combinationis not required, and that the confirmation of the signal may be based onany combination of outputs from any combination of the algorithms 410a-n, 420 a-n, including but not limited to a majority consensus,minority consensus, or even a single indication of the existence of asignal from a single algorithm. Further, although this embodiment showsa particular hardware configuration using a demultiplexer 440, a personof ordinary skill in the art will recognize that many configurations ofhardware, software, or both, may accomplish the same functionality.

FIG. 5 is a block diagram illustrating an exemplary signal analyzer 500,according to an aspect of an embodiment. A signal analysis engine 510receives signal information from the signal detector 300 and/or from thesignal confirmer 400 and analyzes the signal information using one ormore algorithms, which may be stored in an algorithm database 520. Aperson of ordinary skill in the art will recognize that the algorithmsmay comprise a variety of forms, and that a separate database ofalgorithms is just one possible form. The algorithms used will depend onthe type or types of analyses required for a given purpose, and mayinclude, but are not limited to bin-wise processing, Rayleighdistribution analysis, telecommunication signal classification, receiveranomaly detection, transmitter density estimation, transmitter detectionand location, geolocation analysis, telecommunication activityestimation, telecommunication utilization estimation, frequencyutilization estimation, and data interpolation. Each of these algorithmsis described in further detail below. The results of the analyses may bestored in data storage 120. Machine learning algorithms may also be usedin conjunction with, or as part of, these described algorithms.

Some or all of the above-described processing steps may be performed inparallel, either on the same radio frequency signal sample data or onduplicate copies of the original data (that may then be recombined afterprocessing). The system is not limited by the components described, andmay include other components, such as signal filters, and additionalprocessing steps such as transforms (fast Fourier transform, wavelettransform, discrete cosine transform, or other related transforms), andadditional statistical analyses such as calculation of the mean, median,maximum, minimum, standard deviation, variance, skew, kurtosis, and/orother statistical value of a signal's amplitude, or the log of theamplitude, or of the power in each frequency domain sample.

FIG. 6 is a diagram illustrating a variety 600 of sensor systems andcollection platforms for gathering radio frequency signal information. Aradio frequency signal information processing and analysis system 200would be most effective if a broad range of digital radio frequencysignal information is collected from a variety of sensor systems andcollection platforms with different characteristics. Ideally, the arrayof sensor systems and collection platforms would comprise a plurality ofmobile collection platforms and stationary collection platforms, witheach collection platform wherein one of the algorithms used in the isone or more sensor systems. For example, stationary collection platformsmay be any ground-based structure such and building or towers, andideally would be located in geographically diverse locations, and atdifferent altitudes, and with different lines of sight. Ground-basedmobile collection platforms (not shown) may be mobile phones orvehicle-mounted. Aerial collection platforms may comprise satellites606, airplanes 605, cars, or balloons 604, each of might collect datausing different instrumentation, from different altitudes, and alongdifferent flight paths. Antenna types used by the sensor systems on allcollection platforms may comprise directional antennas 601, 603 ornon-directional antennas 602.

Detailed Description of Exemplary Aspects

FIG. 7 is a flow diagram illustrating an exemplary algorithm forbin-wise processing 700, according to an aspect of an embodiment. Data“bins” are created by using transforms such as the Fourier transform(and its various implementations such as fast Fourier transform (FFT) ordiscrete Fourier transform (DFT)), wavelet transform, discrete cosinetransform, or other related transforms 701. In a given frequency bin,the values are averaged or summed over a number of transform-domainsamples to reduce the variance in the signals and noise levels andthereby allow for low or negative signal-to-noise ratio (SNR) radiofrequency signal detection 702. A mean or median value, or otherstatistical measure in each bin may be determined over a number oftransform-domain samples to overcome the effects of a signal that issignificantly stronger or weaker than the average 703. A maximum and/orminimum value, standard deviation, variance, or other statisticalmeasure in each bin over a number of transform-domain samples may bedetermined and used to resolve signals that are continuously present andsignals that are intermittent in time 704. Processing may also find themean, median, maximum, minimum, standard deviation, variance, skew,kurtosis, entropy, and/or other statistical measures, plus the timecomponent for a bin or set of bins in a geographical area, and compareeach statistic to the same statistic computed for that bin or set ofbins over a large area to identify deviations, identify anomalies, anddistinguish signals from noise 705. Bin-by-bin processing may beperformed using the average, median, maximum, minimum, standarddeviation, variance, skew, kurtosis, and/or other statistical valuesover a number of transform-domain samples, to reduce the quantity ofdata in results and expedite processing 706. The number oftransform-domain samples in use may be adjusted to increase or decreasetime resolution as needed and thereby aid in signal detection 707. Binsmay be combined as needed, using a maximum value search or statisticalmeasure to reduce quantity while also detecting the presence of signals708. Transform-domain samples may also be combined or dropped (forexample, using decimation to down-sample the frequency of a signal,using fast Fourier transforms (FFTs) or other transforms, etc.), usingcombination over time to reduce noise and improve signal detection 709,before finally outputting data for further handling. Although thisexemplary bin-wise processing algorithm has been described usingparticular set of steps in a particular order, it is worth noting thatnot all steps may need to be used, and the particular order of stepsgiven in this example may not be required, to produce acceptableresults, depending on the results required and the nature and quality ofthe data being processed.

FIG. 8 is a flow diagram illustrating an exemplary algorithm for aRayleigh distribution analysis 800, according to an aspect of anembodiment. According to the aspect, signals may be detected andclassified by finding the Rayleigh distribution of in-phase andquadrature-phase samples 801, comparing the Rayleigh distribution ofsignals over a frequency band of interest against noise distribution802, and applying thresholds to isolate signals 803. As with othertechniques herein, thresholds may be developed and refined using machinelearning and training datasets, improving operation through theapplication of statistical techniques. According to the “central limittheorem”, a sufficiently large sum of sinusoidal signals will have adistribution that converges around Gaussian noise, and in practice asmall number of equal-amplitude signals still retain a distinguishableKurtosis and thus may be isolate using trained thresholds.

FIG. 9 is a flow diagram illustrating an exemplary algorithm fortelecommunication signal classification 900, according to an aspect ofan embodiment. This technique may be used to detect, identify, andclassify telecommunication signals using image processing techniques inthe frequency-domain and/or time-frequency domain. Image processingalgorithms may be used in multiple frequency and time resolutions toproduce compact datasets 901. These compact datasets may be used astraining datasets to train machine learning algorithms to detect,identify, and classify telecommunication signals 902. For example, a 10MHz LTE signal can be used as training data to refine a model fordetection of 10 MHz (or larger) signals in live data. Training mayutilize multiple different resolutions for either or both the time andfrequency domains, for example to produce more training data from fewerdata sets by narrowing the frequency resolution and using smallerportions of bandwidth for each sample. This training may be used toapply machine learning to future datasets in live signal data 903, forexample to refine thresholds for other algorithms described herein andto improve signal processing results. Input to a machine learningalgorithm may also include features specific to the signal type such asan LTE synchronization symbol based on any one or combination of theprimary or secondary synchronization signals or reference signals fromone or more antenna ports, or demodulated information block messages.Similarly, other wireless signals may be synchronized or partiallydemodulated to derive additional features.

FIG. 10 is a flow diagram illustrating an exemplary algorithm forreceiver anomaly detection 1000, according to an aspect of anembodiment. When a telecommunication signal is received from a givenreceiver, the Kurtosis value may be calculated using only the amplitudevalue of the signal (and not complex values) 1001. This Kurtosis valuemay then be compared to subsequent signals in question 1002 from thesame receiver, and thresholds may be applied to determine whether thesignal in question is an artifact of the sensor hardware (i.e., ananomaly) and should be discarded 1003. These thresholds may be refinedusing machine learning techniques, as described elsewhere. Thisalgorithm may be used to detect spurious signals, aliased signals,and/or signals that are saturating a receiver without contributingadditional useful data. Such anomalous signals may be discarded in orderto reduce sensor load and improve operations on radio frequency signals,thereby reducing the total amount of data to be processed and cleaningup datasets that are used for various techniques described herein 1004.

FIG. 11 is a flow diagram illustrating an exemplary algorithm fortransmitter density estimation 1100, according to an aspect of anembodiment. Depending on the geographical resolution, area covered byeach sample (or filled-in sample data from interpolation), ortransmitter density, estimating the locations of transmitters may notalways be feasible. In these cases, density may be estimated rather thanprecise locations for transmitters in a given geographical area ofinterest. In an initial step 1101, measured signal strength data may bereceived for one or more frequency bands, for example for a specificfrequency or group of frequencies, or for interpolated frequency data,and mapped across a geographical area. This mapping may occur regardlessof whether there is any information available on transmitter locations.Next, a portion of the geographical area in question may be surveyed1102, and the number of transmitters in the surveyed area may be counted1103. The transmitter numbers (and optionally any other observedinformation such as transmitter types or antenna configurations) maythen be correlated with the measured signal strength data 1104,producing a measurement of transmitters-per-signal strength perfrequency or group of frequencies. This may then be applied to theremaining (and presumably low-resolution) geographical area 1105 toestimate the transmitter density based on the measured signal strengthdata.

FIG. 12 is a flow diagram illustrating an exemplary algorithm fortransmitter detection and location 1200, according to an aspect of anembodiment. The first step is to examine multiple signal samples takenfrom different times or look angles at a given frequency or frequencies1201. Samples may then be examined across multiple frequencies or groupsof frequencies 1202, and signals may be correlated in a givengeographical area to determine whether a transmitter location has beenidentified 1203.

FIG. 13 is a flow diagram illustrating an exemplary algorithm forgeolocation analysis 1300, according to an aspect of an embodiment. Whena set of signal data is received 1301, it may be analyzed using eitheran iterative method 1302 or a multilateration method 1303. If an initialtransmitter time of transmission is known, the iterative method 1302 maybe utilized to iteratively fit the timing data for the received signaldataset to the assumed transmitter location. Using the iterative method1302, for each signal sample in the dataset, a time-of-flight iscalculated for each receiving sensor or observation location. Thistime-of-flight information is then used to determine the range from thesignal source to the receiving sensor(s). The multilateral location ofthe transmitter is calculated from the intersection of the range values.This iterative method 1302 utilizes signal timing information forsignals, providing an advantage when multiple sensors are not available;rather than relying on multiple sensors for multilateration, a singlesensor may be moved to take samples at different points in time, sincethe signal transmission timing is known. If the transmitter time oftransmission is unknown, the multilateration method 1303 is usedinstead, which is an expansion of the trilateration method known in theart. In this method, the location of the signal source may be estimatedby using additional measurements from the same sensor or by usingadditional sensors to derive differential ranges. By measuring thetime-of-arrival (TOA) for each signal transmission received, and giventhe known location of the sensor at that time, the range may becalculated for each received signal and therefore arrive at the locationof the transmitter. Capturing multiple consecutive timing signals may beused to reduce errors, particularly with weaker signals or where thereis a large amount of noise or interference. The timing information canbe supplemented with power, phase, and frequency information topotentially increase accuracy. What it is supplemented with (power,phase, frequency) depends on the properties of the sensor (e.g., phasedifferences require multiple antennas and phase-matched receive paths inthe sensor hardware) and the collection platform (e.g., an aircraft orspacecraft with a sensor attached will induce a Doppler shift infrequency with respect to a stationary terrestrial transmitter). Inparticular in certain signal environments in which power is applied assupplemental information, the estimated error of the calculatedtransmitter location can be refined using the extent of the observationarea and signal strength.

FIG. 14 is a flow diagram illustrating an exemplary algorithm forgeolocation analysis using an alternate method 1400, according to anaspect of an embodiment. Geolocation may be determined by receivingsignal information over multiple observation locations 1401 anddetermining the best location estimate based on highest received signalpower 1402. The error associated with this estimate may be determined byexamining detected signal power and the geographical extent over whichthe signal is observed 1403. This method of using received signal powercan be applied stand-alone, or it can be used to refine timing-basedmethods.

FIG. 15 is a flow diagram illustrating an exemplary algorithm fortelecommunication activity estimation 1500, according to an aspect of anembodiment. According to this technique, telecommunication activity maybe estimated within a given frequency band of interest, and may then becompared against the total capacity of that band to identify overalltelecommunication usage. For each signal received 1501 within a sampleset, the portion of time that the signal energy level is above the noisefloor may be estimated, and the frequency of instances where the energylevel exceeds the noise floor may be determined 1502. Then a Rayleighdistribution of estimated values may be produced 1503, and correlated tothe amount of activity in the frequency band vs. the overallcommunication capacity of that band 1504.

FIG. 16 is a flow diagram illustrating an exemplary algorithm fortelecommunication utilization estimation 1600, according to an aspect ofan embodiment. According to this technique, the portion of a givenfrequency band that is being used vs. the total bandwidth may beestimated, by detecting signals that occupy parts of a defined frequencyband and estimating the bandwidth consumed out of the total bandwidthavailable. First, for each received signal 1601, the signal edges may bedetected 1602 by estimating the mean value along the time domain toobtain a single set of data in the frequency domain, and thencalculating the gradient. A large magnitude in the gradient indicatesthe edge of a signal in the frequency domain, so by applying a thresholdvalue edges may be determined for each signal. The bandwidth used by thesignal is estimated compared to the overall frequency band 1602, andthen the estimated bandwidth of all signals within the band may becompiled to determine the overall bandwidth utilization 1603.

FIG. 17 is a flow diagram illustrating an exemplary algorithm forfrequency utilization estimation 1700, according to an aspect of anembodiment. Frequency utilization may be estimated using two methods.The first method 1701 is used where a frequency band has been defined.According to the first method 1701, machine learning algorithms are usedto identify clustering of frequencies. The second method 1702 is usedwhere a frequency band has not been defined, according to the secondmethod 1702, a convolutional filter is tuned in time and frequencycharacteristics to a specific signal type. The filter is convolved overa large time-frequency space, using peaks of the result to identify asignal or signals of interest, from which utilization over the largerfrequency can be estimated.

FIG. 18 is a flow diagram illustrating an exemplary algorithm 1800 forsample data interpolation, according to an aspect of an embodiment. Datainterpolation is used to fill in gaps in data by combining samples inthe time and frequency domains. In the time domain, a variety of factorssuch as system sample rate, sweep rate, instantaneous bandwidth,platform speed or dynamics (such as altitude or angle changes), orspatial Nyquist sampling, samples may be combined to identify and fillin gaps 1801. Algorithms used to combine the samples may include, butare not limited to, combining data by finding the mean, median, maximum,minimum, variance, skew, and kurtosis for the amplitude, or log of theamplitude, power, or Kurtosis across a period of time. For a movingplatform, such as a satellite, aircraft, or other vehicle-mountedsensor, the spatial Nyquist sampling criterion may be taken intoconsideration as well. Samples may also be combined in the frequencydomain 1802, including samples in frequency bins that may notnecessarily be adjacent but may be meaningful when combined (forexample, frequencies that are related by allocation, such asnon-adjacent frequency bands used by the sample mobile networkoperator). Once the samples are combined, missing data may be filled inby incorporating additional information transmitter location, antennapattern or configuration, or transmitter power or orientation 1803. Forexample, a mobile sensor may follow a programmed route (such as a flightpath for an aircraft or satellite), but the sensor data may have gapsbetween route segments. Interpolation may be used to fill in these gapswith additional data, for example using techniques such as in-paintingor kriging. Results may be improved through incorporation of transmitterlocation information, for example using techniques described above orusing known (for example, fixed) transmitter locations. This data maythen be compared to the data collected by the mobile sensor along itsroute, filling in gaps where possible. When multiple data points areavailable already, reverse propagation may be used 1804 to determinemissing antenna or transmitter configuration information. Extending thisconcept, when multiple measured data points are available, thisinformation can be applied to inform and improve propagation modeling1804.

Computer Architecture

Generally, the techniques disclosed herein may be implemented onhardware or a combination of software and hardware. For example, theymay be implemented in an operating system kernel, in a separate userprocess, in a library package bound into network applications, on aspecially constructed machine, on a field programmable gate array(FPGA), on an application-specific integrated circuit (ASIC), or on anetwork interface card.

Software/hardware hybrid implementations of at least some of the aspectsdisclosed herein may be implemented on a programmable network-residentmachine (which should be understood to include intermittently connectednetwork-aware machines) selectively activated or reconfigured by acomputer program stored in memory. Such network devices may havemultiple network interfaces that may be configured or designed toutilize different types of network communication protocols. A generalarchitecture for some of these machines may be described herein in orderto illustrate one or more exemplary means by which a given unit offunctionality may be implemented. According to specific aspects, atleast some of the features or functionalities of the various aspectsdisclosed herein may be implemented on one or more general-purposecomputers associated with one or more networks, such as for example anend-user computing device, a client computer, a network server or otherserver system, a mobile computing device (e.g., tablet computing device,mobile phone, smartphone, laptop, or other appropriate computingdevice), a consumer electronic device, a music player, or any othersuitable electronic device, router, switch, or other suitable device, orany combination thereof. In at least some aspects, at least some of thefeatures or functionalities of the various aspects disclosed herein maybe implemented in one or more virtualized computing environments (e.g.,network computing clouds, virtual machines hosted on one or morephysical computing machines, or other appropriate virtual environments).

In other cases, purpose-built computing devices designed to performspecific functions may be used. In some cases, the purpose-builtcomputing devices may comprise application-specific integrated circuits,field programmable gate arrays (FPGAs), or any other combination ofhardware and software that may be built or programmed for a particularpurpose. This is particularly the case where high speed or real timeperformance is required, or where limitations are placed on physicalsize, memory capacity, processor speed due to space constraints,budgetary constraints, and the like. Such purpose-built computingdevices may be embedded into other devices (where they are often called“embedded systems”).

Referring now to FIG. 19 , there is shown a block diagram depicting anexemplary computing device 10 suitable for implementing at least aportion of the features or functionalities disclosed herein. Computingdevice 10 may be, for example, any one of the computing machines listedin the previous paragraph, or indeed any other electronic device capableof executing software- or hardware-based instructions according to oneor more programs stored in memory.

Computing device 10 may be configured to communicate with a plurality ofother computing devices, such as clients or servers, over communicationsnetworks such as a wide area network a metropolitan area network, alocal area network, a wireless network, the Internet, or any othernetwork, using known protocols for such communication, whether wirelessor wired.

In one aspect, computing device 10 includes one or more centralprocessing units (CPU) 12, one or more interfaces 15, and one or morebusses 14 (such as a peripheral component interconnect (PCI) bus). Whenacting under the control of appropriate software or firmware, CPU 12 maybe responsible for implementing specific functions associated with thefunctions of a specifically configured computing device or machine. Forexample, in at least one aspect, a computing device 10 may be configuredor designed to function as a server system utilizing CPU 12, localmemory 11 and/or remote memory 16, and interface(s) 15. In at least oneaspect, CPU 12 may be caused to perform one or more of the differenttypes of functions and/or operations under the control of softwaremodules or components, which for example, may include an operatingsystem and any appropriate applications software, drivers, and the like.

CPU 12 may include one or more processors 13 such as, for example, aprocessor from one of the Intel, ARM, Qualcomm, and AMD families ofmicroprocessors or microcontrollers or digital signal processors. Insome aspects, processors 13 may include specially designed hardware suchas application-specific integrated circuits (ASICs), electricallyerasable programmable read-only memories (EEPROMs), field-programmablegate arrays (FPGAs), and so forth, for controlling operations ofcomputing device 10. In a particular aspect, a local memory 11 (such asnon-volatile random access memory (RAM) and/or read-only memory (ROM),including for example one or more levels of cached memory) may also formpart of CPU 12. However, there are many different ways in which memorymay be coupled to system 10. Memory 11 may be used for a variety ofpurposes such as, for example, caching and/or storing data, programminginstructions, and the like. It should be further appreciated that CPU 12may be one of a variety of system-on-a-chip (SOC) type hardware that mayinclude additional hardware such as memory or graphics processing chips,such as a QUALCOMM SNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becomingincreasingly common in the art, such as for use in mobile devices orintegrated devices.

As used herein, the term “processor” is not limited merely to thoseintegrated circuits referred to in the art as a processor, a mobileprocessor, or a microprocessor, but broadly refers to a microcontroller,a microcomputer, a programmable logic controller, anapplication-specific integrated circuit, and any other programmablecircuit.

In one aspect, interfaces 15 are provided as network interface cards(NICs). Generally, NICs control the sending and receiving of datapackets over a computer network; other types of interfaces 15 may forexample support other peripherals used with computing device 10. Amongthe interfaces that may be provided are Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces,graphics interfaces, and the like. In addition, various types ofinterfaces may be provided such as, for example, universal serial bus(USB), Serial, Ethernet, FIREWIRE™, THUNDERBOLT™, PCI, parallel, radiofrequency, BLUETOOTH™, near-field communications (e.g., using near-fieldmagnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fast Ethernetinterfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) or externalSATA (ESATA) interfaces, high-definition multimedia interface (HDMI),digital visual interface (DVI), analog or digital audio interfaces,asynchronous transfer mode (ATM) interfaces, high-speed serial interface(HSSI) interfaces, Point of Sale (POS) interfaces, fiber datadistributed interfaces (FDDIs), and the like. Generally, such interfaces15 may include physical ports appropriate for communication withappropriate media. In some cases, they may also include an independentprocessor (such as a dedicated audio or video processor, as is common inthe art for high-fidelity A/V hardware interfaces) and, in someinstances, volatile and/or non-volatile memory (e.g., RAM).

Although the system shown in FIG. 19 illustrates one specificarchitecture for a computing device 10 for implementing one or more ofthe aspects described herein, it is by no means the only devicearchitecture on which at least a portion of the features and techniquesdescribed herein may be implemented. For example, architectures havingone or any number of processors 13 may be used, and such processors 13may be present in a single device or distributed among any number ofdevices. In one aspect, a single processor 13 handles communications aswell as routing computations, while in other aspects a separatededicated communications processor may be provided. In various aspects,different types of features or functionalities may be implemented in asystem according to the aspect that includes a client device (such as atablet device or smartphone running client software) and server systems(such as a server system described in more detail below).

Regardless of network device configuration, the system of an aspect mayemploy one or more memories or memory modules (such as, for example,remote memory block 16 and local memory 11) configured to store data,program instructions for the general-purpose network operations, orother information relating to the functionality of the aspects describedherein (or any combinations of the above). Program instructions maycontrol execution of or comprise an operating system and/or one or moreapplications, for example. Memory 16 or memories 11, 16 may also beconfigured to store data structures, configuration data, encryptiondata, historical system operations information, or any other specific orgeneric non-program information described herein.

Because such information and program instructions may be employed toimplement one or more systems or methods described herein, at least somenetwork device aspects may include nontransitory machine-readablestorage media, which, for example, may be configured or designed tostore program instructions, state information, and the like forperforming various operations described herein. Examples of suchnontransitory machine-readable storage media include, but are notlimited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks; magneto-optical mediasuch as optical disks, and hardware devices that are speciallyconfigured to store and perform program instructions, such as read-onlymemory devices (ROM), flash memory (as is common in mobile devices andintegrated systems), solid state drives (SSD) and “hybrid SSD” storagedrives that may combine physical components of solid state and hard diskdrives in a single hardware device (as are becoming increasingly commonin the art with regard to personal computers), memristor memory, randomaccess memory (RAM), and the like. It should be appreciated that suchstorage means may be integral and non-removable (such as RAM hardwaremodules that may be soldered onto a motherboard or otherwise integratedinto an electronic device), or they may be removable such as swappableflash memory modules (such as “thumb drives” or other removable mediadesigned for rapidly exchanging physical storage devices),“hot-swappable” hard disk drives or solid state drives, removableoptical storage discs, or other such removable media, and that suchintegral and removable storage media may be utilized interchangeably.Examples of program instructions include both object code, such as maybe produced by a compiler, machine code, such as may be produced by anassembler or a linker, byte code, such as may be generated by forexample a JAVA™ compiler and may be executed using a Java virtualmachine or equivalent, or files containing higher level code that may beexecuted by the computer using an interpreter (for example, scriptswritten in Python, Perl, Ruby, Groovy, or any other scripting language).

In some aspects, systems may be implemented on a standalone computingsystem. Referring now to FIG. 20 , there is shown a block diagramdepicting a typical exemplary architecture of one or more aspects orcomponents thereof on a standalone computing system. Computing device 20includes processors 21 that may run software that carry out one or morefunctions or applications of aspects, such as for example a clientapplication 24. Processors 21 may carry out computing instructions undercontrol of an operating system 22 such as, for example, a version ofMICROSOFT WINDOWS™ operating system, APPLE macOS™ or iOS™ operatingsystems, some variety of the Linux operating system, ANDROID™ operatingsystem, or the like. In many cases, one or more shared services 23 maybe operable in system 20, and may be useful for providing commonservices to client applications 24. Services 23 may for example beWINDOWS™ services, user-space common services in a Linux environment, orany other type of common service architecture used with operating system21. Input devices 28 may be of any type suitable for receiving userinput, including for example a keyboard, touchscreen, microphone (forexample, for voice input), mouse, touchpad, trackball, or anycombination thereof. Output devices 27 may be of any type suitable forproviding output to one or more users, whether remote or local to system20, and may include for example one or more screens for visual output,speakers, printers, or any combination thereof. Memory 25 may berandom-access memory having any structure and architecture known in theart, for use by processors 21, for example to run software. Storagedevices 26 may be any magnetic, optical, mechanical, memristor, orelectrical storage device for storage of data in digital form (such asthose described above, referring to FIG. 19 ). Examples of storagedevices 26 include flash memory, magnetic hard drive, CD-ROM, and/or thelike.

In some aspects, systems may be implemented on a distributed computingnetwork, such as one having any number of clients and/or servers.Referring now to FIG. 21 , there is shown a block diagram depicting anexemplary architecture 30 for implementing at least a portion of asystem according to an aspect of an embodiment on a distributedcomputing network. According to the aspect, any number of clients 33 maybe provided. Each client 33 may run software for implementingclient-side portions of a system; clients may comprise a system 20 suchas that illustrated in FIG. 20 . In addition, any number of servers 32may be provided for handling requests received from one or more clients33. Clients 33 and servers 32 may communicate with one another via oneor more electronic networks 31, which may be in various aspects any ofthe Internet, a wide area network, a mobile telephony network (such asCDMA or GSM cellular networks), a wireless network (such as WiFi, WiMAX,LTE, and so forth), or a local area network (or indeed any networktopology known in the art; the aspect does not prefer any one networktopology over any other). Networks 31 may be implemented using any knownnetwork protocols, including for example wired and/or wirelessprotocols.

In addition, in some aspects, servers 32 may call external services 37when needed to obtain additional information, or to refer to additionaldata concerning a particular call. Communications with external services37 may take place, for example, via one or more networks 31. In variousaspects, external services 37 may comprise web-enabled services orfunctionality related to or installed on the hardware device itself. Forexample, in one aspect where client applications 24 are implemented on asmartphone or other electronic device, client applications 24 may obtaininformation stored in a server system 32 in the cloud or on an externalservice 37 deployed on one or more of a particular enterprise's oruser's premises.

In some aspects, clients 33 or servers 32 (or both) may make use of oneor more specialized services or appliances that may be deployed locallyor remotely across one or more networks 31. For example, one or moredatabases 34 may be used or referred to by one or more aspects. Itshould be understood by one having ordinary skill in the art thatdatabases 34 may be arranged in a wide variety of architectures andusing a wide variety of data access and manipulation means. For example,in various aspects one or more databases 34 may comprise a relationaldatabase system using a structured query language (SQL), while othersmay comprise an alternative data storage technology such as thosereferred to in the art as “NoSQL” (for example, HADOOP CASSANDRA™,GOOGLE BIGTABLE™, and so forth). In some aspects, variant databasearchitectures such as column-oriented databases, in-memory databases,clustered databases, distributed databases, or even flat file datarepositories may be used according to the aspect. It will be appreciatedby one having ordinary skill in the art that any combination of known orfuture database technologies may be used as appropriate, unless aspecific database technology or a specific arrangement of components isspecified for a particular aspect described herein. Moreover, it shouldbe appreciated that the term “database” as used herein may refer to aphysical database machine, a cluster of machines acting as a singledatabase system, or a logical database within an overall databasemanagement system. Unless a specific meaning is specified for a givenuse of the term “database”, it should be construed to mean any of thesesenses of the word, all of which are understood as a plain meaning ofthe term “database” by those having ordinary skill in the art.

Similarly, some aspects may make use of one or more security systems 36and configuration systems 35. Security and configuration management arecommon information technology (IT) and web functions, and some amount ofeach are generally associated with any IT or web systems. It should beunderstood by one having ordinary skill in the art that anyconfiguration or security subsystems known in the art now or in thefuture may be used in conjunction with aspects without limitation,unless a specific security 36 or configuration system 35 or approach isspecifically required by the description of any specific aspect.

FIG. 22 shows an exemplary overview of a computing device 40 as may beused in any of the various locations throughout the system. It isexemplary of any computer that may execute code to process data. Variousmodifications and changes may be made to computing device 40 withoutdeparting from the broader scope of the system disclosed herein. Centralprocessor unit (CPU) 41 is connected to bus 42, to which bus is alsoconnected memory 43, nonvolatile memory 44, display 47, input/output(I/O) unit 48, and network interface card (NIC) 53. I/O unit 48 may,typically, be connected to keyboard 49, pointing device 50, hard disk52, and real-time clock 51. NIC 53 connects to network 54, which may bethe Internet or a local network, which local network may or may not haveconnections to the Internet. Also shown as part of system 40 is powersupply unit 45 connected, in this example, to a main alternating current(AC) supply 46. Not shown are batteries that could be present, and manyother devices and modifications that are well known but are notapplicable to the specific novel functions of the current systemdisclosed herein. It should be appreciated that some or all componentsillustrated may be combined, such as in various integrated applications,for example Qualcomm or Samsung system-on-a-chip (SOC) devices, orwhenever it may be appropriate to combine multiple capabilities orfunctions into a single hardware device (for instance, in mobile devicessuch as smartphones, video game consoles, in-vehicle computing devicessuch as navigation or multimedia systems in automobiles, or otherintegrated hardware devices).

In various aspects, functionality for implementing systems or methods ofvarious aspects may be distributed among any number of client and/orserver components. For example, various software modules may beimplemented for performing various functions in connection with thesystem of any particular aspect, and such modules may be variouslyimplemented to run on server and/or client components.

The skilled person will be aware of a range of possible modifications ofthe various aspects described above. Accordingly, the present inventionis defined by the claims and their equivalents.

What is claimed is:
 1. A system for estimating telecommunicationbandwidth utilization, comprising: a computing device comprising amemory and a processor; and a telecommunication utilization estimator,comprising a plurality of programming instructions stored in the memoryof, and operating on the processor of, the computing device, wherein theplurality of programming instructions, when operating on the processor,causes the computing device to: receive signal information for aplurality of telecommunication signals in an overall frequency band; foreach signal of the plurality of telecommunication signals, detect thefrequency edges of the signal by: estimating a mean value along the timedomain of the signal; using the estimated mean value along the timedomain to obtain a set of data in the frequency domain; calculating agradient on the set of data in the frequency domain; detecting thefrequency edges of the signal by applying a threshold value to thegradient wherein a gradient in excess of the threshold value indicates afrequency edge; and determining a bandwidth of the signal based on thefrequency edges; for each signal, estimate the bandwidth of that signalcompared to the overall frequency band; and compile the estimatedbandwidths of all of the plurality of telecommunication signals withinthe overall frequency band to determine a total band utilization of theplurality of telecommunication signals within the overall frequencyband.
 2. A method for estimating telecommunication bandwidthutilization, comprising the steps of: receiving signal information for aplurality of telecommunication signals in an overall frequency band; foreach signal of the plurality of telecommunication signals, detecting thefrequency edges of the signal by: estimating a mean value along the timedomain of the signal; using the estimated mean value along the timedomain to obtain a set of data in the frequency domain; calculating agradient on the set of data in the frequency domain; detecting thefrequency edges of the signal by applying a threshold value to thegradient wherein a gradient in excess of the threshold value indicates afrequency edge; and determining a bandwidth of the signal based on thefrequency edges; for each signal, estimating the bandwidth of thatsignal compared to the overall frequency band; and compiling theestimated bandwidths of all of the plurality of telecommunicationsignals within the overall frequency band to determine a total bandutilization of the plurality of telecommunication signals within theoverall frequency band.